The OpenAI Token Counter is a free online tool that helps developers accurately count the number of tokens in text for GPT models. Whether you're building ChatGPT applications, optimizing prompt lengths, or estimating API call costs, this tool provides accurate token counts.
What is a Token?
A Token is the basic unit that Large Language Models (LLMs) use to process text. OpenAI's models use the BPE (Byte Pair Encoding) tokenization algorithm to split text into tokens. In English text, 1 token is approximately 4 characters or 0.75 words. In Chinese text, 1 token is approximately 1-2 characters. Different models use different tokenizers, so the same text may have different token counts across models.
Supported Models & Tokenizers
GPT-4o / o1 / o3-mini: Uses o200k_base tokenizer, more efficient for multilingual text
Claude 3.5 Sonnet: Anthropic's tokenization method
Gemini 1.5 Pro: Google's tokenization method
Core Features
Real-time Counting: Count as you type, no button clicks needed
Multi-Model Comparison: See token counts across all models at once
Cost Estimation: Auto-calculate API costs based on official model pricing
Batch Analysis: Per-line token statistics for multi-line text
Privacy Protection: Pure frontend processing, no data uploads
How to Optimize Token Usage
Reducing token consumption can significantly lower API costs. Here are some optimization tips:
Use concise prompts, avoid redundant descriptions
English is more token-efficient than Chinese for system prompts
GPT-4o's o200k_base tokenizer is more efficient than cl100k_base
Set max_tokens appropriately to avoid excessive output generation
For simple tasks, use GPT-3.5 Turbo or GPT-4o mini instead of GPT-4
Token Counting Methodology
This tool implements the core logic of the BPE tokenization algorithm. For English text, it performs accurate character-level BPE tokenization. For Chinese text, it estimates based on Unicode encoding ranges and common tokenization rules. Accuracy: English 99%+, Chinese 95-98%. For 100% precise counting, we recommend using OpenAI's official tiktoken Python library.
Use Cases
Estimate token consumption when developing ChatGPT plugins and applications
Optimize prompt length to avoid exceeding context window limits
Budget API call costs and plan monthly spending
Compare token efficiency across different models
Batch evaluate document token consumption
Frequently Asked Questions
What is a Token?▼
A Token is the basic unit that LLMs use to process text. In English, 1 token is approximately 4 characters or 0.75 words. In Chinese, 1 token is approximately 1-2 characters. Different models use different tokenizers, so the same text may have different token counts across models.
Which models does the Token Counter support?▼
Supports GPT-4o, GPT-4 Turbo, GPT-4, GPT-3.5 Turbo, o1, o3-mini, Claude 3.5 Sonnet, Gemini 1.5 Pro and other mainstream models. Each model uses its corresponding tokenizer for accurate counting.
Is the token count accurate?▼
This tool uses the same BPE tokenization algorithm as OpenAI, achieving 99%+ accuracy for English text. For Chinese text, accuracy is approximately 95-98% due to complex tokenization rules. We recommend verifying against OpenAI's official tiktoken library for production use.
How do I estimate API call costs?▼
After entering text, the tool automatically calculates token counts and estimates single-call costs based on each model's official pricing (input/output per million tokens). You can also set daily call counts to estimate monthly costs.
Is my text data safe?▼
Completely safe. This tool runs entirely in your browser. All token calculations are performed client-side. No text data is ever uploaded to any server, protecting your privacy and sensitive information.
Why do different models show different token counts?▼
Different models use different tokenizers. GPT-4 series uses cl100k_base, GPT-4o uses o200k_base, and Claude uses its own tokenization method. The tokenizer determines how text is split into tokens, so the same text will have different token counts across models.
Does it support batch text analysis?▼
Yes. You can enter multi-line text and the tool will count tokens for each line separately, plus provide a total. Ideal for batch evaluating prompt lengths or document token consumption.